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The field of Supply Chain Management (SCM) has mainly focused on applications for large firms, where significant amount of theory has been developed in the last decades. Little attention has been received by micro and small enterprises (MSEs) that in Latin America represent approximately 99% of all businesses and are the key for the development of the economy, employment, and growth of the region. Due to MSEs' lack of productivity, only a fraction of them survive and thus contribute to Latin America's economic growth. In this chapter, we discuss the connection between MSEs' productivity growth and SCM. We present key takeaways from the literature and summarized different research approaches used to study this emerging field, specifically related to the impact of the size of the company, the use of surveys to gather data, and the importance of field interventions. We also present a large-scale project (i.e., MIT GeneSys) that focuses on improving survival of MSEs in developing countries and discuss some preliminary learnings gained via conducting shadowing/immersion of ∼250 MSEs from Mexico, Colombia, Chile, Ecuador, Peru, and Bolivia. We conclude the chapter by presenting some recommendations for the future research agenda for the emerging field of SCM for MSEs in Latin America.

The key performance indicators (KPIs) are frequently used in organizations, and they help to transmit the strategy at all levels of the organization. However, the implementation of these indicators in small- and medium-sized companies remains a challenge. Many studies reveal two challenges faced by these firms, the lack of knowledge about the KPIs and the lack of alignment of these with the business strategy. For this reason, this chapter investigated the current level of knowledge about KPIs in managers of small and medium enterprises in the wood and timber sector in Peru.

The level of knowledge was measured using the framework of Bloom's Taxonomy in 21 firms. The use and importance that managers assign to performance indicators were evaluated, in order to identify gaps that exist between the strategy and its use.

The results of a survey study show a high degree of variability in the knowledge of KPI-related concepts as well as an average low level of usage. The importance attributed to KPIs was seen as a necessary but not sufficient condition for attaining higher levels of KPI usage.

Micro and small enterprises (MSEs) represent 99% of Peruvian firms, contribute 42% of Peru's Gross Domestic Product, and employ half of the country's labor force. Despite their relevance for the Peruvian Economy, they have low survival rates and are characterized by low productivity and processes inefficiencies. This chapter explores whether the adoption of Business and Supply Chain Management (SCM) practices influences MSEs' performance. We conducted a field study using data from 50 MSEs located in Piura, Peru, specifically from trade, service, and manufacturing sectors. We used the data collection guidelines from the MIT GeneSys to measure the firms' adoption of Business and SCM practices. Our results show that MSEs with higher adoption of Financial Planning, Supply Chain Planning, Supplier Relationship Management, Marketing, Procurement, and Stock Control practices are more likely to have higher revenue (i.e., sales). In addition, a multiple regression analysis reveals that while SCM practices do not seem to directly explain productivity growth in MSEs (as business practices do), they, however, seem to influence the performance of business practices, and, thus, have an indirect effect on the productivity growth of MSEs.

Since 2016, organized retailers in Colombia have struggled against a new retail format: Hard-discount stores. This sales channel fulfills essential shopping basket products with consistent low prices. To be competitive and preserve their market position, organized retailers must improve their processes and their pricing decisions. Promotions and discounts have been considered as an effective alternative to compete. This study analyzes the impact of joint prices decisions over the individual and global financial key performance indicators when a collaborative strategy is adopted. Our case study comprises a supermarket chain Colombian retailer and a consumer packaged-goods manufacturer to analyze its supply chain performance. The analysis considers different product categories (food, personal care, and cosmetics) and country regions. The results highlight that benefits are unequally distributed along different echelons and supply chain performance is affected when pricing decisions are made independently.

With the increasing urbanization rates in emerging countries such as the ones in Latin America and the Caribbean, urban logistics solutions and initiatives are widely needed. Urban planners often consider only passenger transportation and leave freight transportation unattended, thus increasing externalities and degrading the transportation of goods. This chapter presents three urban logistics solutions, which intend to tackle problems related to urbanization and last mile delivery operations challenges by evaluating location models for loading and unloading bays, urban transfer centers location models, and freight trip generation models. The presented solutions were proposed by several researchers of the Institute of Innovation in Productivity and Logistics CATENA-USFQ over the last four years and remain theoretical at the moment. However, we present estimated results of potential implementations in three districts of Quito: Historic Center, Entertainment District, and Corporate District. This chapter not only presents the mentioned urban logistics solutions in Quito but also gives an overview of the followed methodology, which can be replicated in countries and cities of similar characteristics of the region.

This chapter discusses a collaborative strategy for noncompetitive small- and medium-sized enterprises (SME's) aiming to reduce their logistics costs by means of a joint replenishment of multiple items. The proposed approach is an extension of the classical joint replenishment problem, named as a Stochastic Collaborative Joint Replenishment problem (S-CJRP) because it considers stochastic demand, warehouse and transport capacity constraints, and multiple buyers and vendors. Operating this method implies three main challenges: (1) determining the frequency with which each buyer should replenish the products; (2) allocating investments and benefits between partnering buyers; and (3) deciding whether to coordinate the supply chain internally or outsource its coordination. The S-CJRP is solved through a heuristic approach, which deals with uses of the Shapley Value Function to allocate the investments and benefits, and it explores the coordination through several simulation scenarios, all of which exhibit prospective cost reductions in inventory management. Preliminary results show that third-party logistics providers could be a valuable resource in coordinating SMEs along a supply chain.

Brazil has been increasing its participation in the international trade market, mainly due to agricultural and forestry products, as in the case of soybeans and cellulose. This growth led to the expansion of the logistics infrastructure and its use. An important example of this trend is the port of São Luís, MA, in northern Brazil, which saw an increase in exports via rail (more than 200% growth in 6 years) and, consequently, an increase in the circulation of trains within its port complex.

This work proposes a mixed-integer linear programming model for the daily train scheduling problem at this port. All trains operated by VLI, a logistics company, are scheduled to minimize the departure times in order to improve the dwell time of freight train cars.

The railroad system in this Brazilian port consists of two classification yards, five terminals and a double-track railway for circulation. Different products such as grains, minerals, cellulose, and fuels are transported. The model also incorporates different operations at terminals and occupation restrictions due to maintenance and the physical flow of other third-party logistics companies. These features are modeled through a preprocessing step. In this phase, a series of auxiliary sets are defined to simplify constraints, circulation options are mapped, and the double-track is divided into segments based on the transit time with the objective to control track occupation.

This preprocess step also reduces the model complexity and, consequently, the computational time to solve it, as shown in the numerical tests using real-world operational data.

The main gains of the project were a reference train timetable for peak days, standardization of train crossing options, and a support tool for traffic adjustments with other rail operators.

In the context of increasing competition between chained retailers and family-owned retailers, it is key to understand the customer's format choice. Using a logistics regression (i.e., binary logit) model, we explain customers' preference to buy in supermarkets or in small-scale, mom-and-pop stores like nanostores. We collect a representative sample of over 110 surveys from customers in the district of Surco, Lima, Perú, which is a representative area of the features of Lima's residents. We asked customers to focus on analyzing their preference between two retail formats: modern channel (i.e., big-box retailers, supermarkets, and hypermarkets) and traditional channel (i.e., mom-and-pop stores, nanostores). Our surveys included factors pertaining retail format attributes as well as factors related to the purchasing process. The results showed that time available for purchase and a comparatively better perceived service at a mom-and-pop store (i.e., nanostore) are significant factors that explain a higher probability of selecting these retailers, while a better store's ambience benefits more supermarkets. The overall discrete choice model is able to explain 65% of the variance using pseudo R-squared of the actual format choice decisions.